Digital Underwriting: Riding the Insurance Transformation Wave With MongoDB

Jeff Needham and Silvio Sola


In our previous article about digital underwriting, “A Digital Transformation Wave in Insurance,” we covered the main challenges insurers face when it comes to streamlining and modernizing their underwriting processes, along with key areas that can be improved by leveraging the power of data and artificial intelligence.

We analyzed how modern IT trends require a complete redesign of manual underwriting processes to enable insurers to leverage new market opportunities and stay relevant in an ever-changing risk landscape. We explored how the full underwriting workflow — from the intake of new cases to risk assessment and pricing — can be redesigned to ease the burden on underwriting teams and enable them to focus on what matters most.

In this second article, we’ll expand on how new technology paradigms can support transformation initiatives in this space and describe the pivotal role MongoDB plays in disrupting the industry.

The importance of data and new technology paradigms

For digital underwriting transformation initiatives to succeed, organizations must move away from monolithic applications, where data is siloed and functionality is fragmented across different technologies. However, as many organizations have additionally come to realize, lifting and shifting these monolithic applications to the cloud does not automatically bring them closer to achieving their digital objectives.

Organizations that are successful in their transformation efforts are increasingly adopting MACH architecture principles to modernize their application stacks. The acronym stands for Microservices, API-first, Cloud-based, and Headless, and, combined, those principles enable developers to leverage best-of-breed technology and build services that can be used across multiple different business workflows and applications.

These principles allow software delivery teams to reduce the time it takes to deliver new business features and promote significant reuse and flexibility far beyond the monolithic applications that pre-date them.

From an insurance perspective, this approach enables underwriting systems to be decoupled into business and capability domains, each working independently, yet sharing data as part of an event-driven design and microservices architecture. Often overlooked, shared capability domains can provide significant value to an organization's business domains, as seen in the visual below.

Diagram of the categories that fall into Business Domains vs. Capability Domains. Business domains include Agent, Customer, Pricing, Policy, Clain, and Billing. Capability Domains include Notes, Decision Support, Alerts, Documents, Tasks, and User Profile.
Figure 1.   Key business and capability domains.

Each function of the application should be owned by the team holding expertise in that particular domain and be loosely coupled with the others. Services can communicate with each other via APIs, as well as listen for and consume one another's events.

Building a domain-based data modernization strategy can also enable a phased migration away from legacy systems. This allows for immediate realization of the organization's digital objectives, without first engaging in a costly and timely legacy system replacement effort.

An event-driven, and API-enabled architecture allows for real-time data processing, a core component of digital enablement.

Diagram of microservices and event-driven architecture. Legacy systems flow in between APIs & Microservices. APIs & Microservices then flows into Events, which then flows back into APIs & Microservices.
Figure 2.   Microservices and event-driven architecture.

Read the previous post in this series, "A Digital Transformation Wave in Insurance."

Decision support services

Once monolithic systems are decomposed into finer-grained domains and services and begin interacting via APIs and events, it is possible to focus on the most crucial component that brings all of them together — the decision support domain. Its role is to streamline and, where possible, automate underwriting and other decision-making processes that traditionally require heavy administrative and manual work in order to reduce operational expenses and enable critical underwriting staff to focus on highest priority work.

Effective underwriting processes require pulling together multiple teams and capability domains (e.g., claim, customer, pricing, billing, and so forth) to be able to reach a decision on whether to insure a new customer or define an adequate pricing and coverage model, among other factors. A decision support engine has the power to fully automate those steps by automatically triggering workflows based on specific events (e.g., a new claim is submitted in the system) as part of the event-driven design referenced earlier to enable real-time decision making.

Why MongoDB

With the added burden of integrating and working with various sources of data — from APIs to events to legacy databases — and doing so in real time, software delivery teams need a developer data platform that allows them to tame complexity, not increase it.

Refactoring systems that have been around for decades is not an easy feat and typically results in multi-year transformation initiatives. MongoDB provides insurers with the same ACID capabilities of relational databases, while introducing new tools and flexibility to ease transformation by increasing developer productivity and fully supporting the MACH principles.

The MongoDB application data model

MongoDB provides a developer data platform leveraged by some of the world’s largest insurers. It possesses key capabilities that allow it to:

  • Integrate legacy siloed data into a new single view. The flexibility of the document model enables the integration of separate, legacy data stores into an elegant, single-view data model that reduces rather than increases complexity. Without the complexities of another canonical, relational model, application development and data migration efforts are dramatically simplified, and delivery timelines shortened.

  • Manage the full lifecycle of containerized applications. MongoDB’s Enterprise Operator for Kubernetes lets you deploy and manage the full lifecycle of applications and MongoDB clusters from your Kubernetes environment for a consistent experience regardless of an on-premises, hybrid, or public cloud topology.

  • Automate workflows, leveraging events in real-time. MongoDB provides the data persistence at the heart of event-driven architectures with connectors and tools that make it easy to move data between systems (e.g., MongoDB Connector for Apache Kafka), providing a clear separation between automated underwriting workflows and those requiring manual intervention.

  • Enable business agility using DevOps methodologies. MongoDB Atlas, the global cloud database for MongoDB, provides users with quick access to fully managed and automated databases. This approach allows development teams to add new microservices and make changes to application components much more quickly. It also saves a substantial amount of operations effort, since database administrators are not required in every sprint to make and manage changes.

  • Work quickly with complex data. Developers can analyze many types of data directly within the database, using the MongoDB Aggregation Pipeline framework. And, with the power of Atlas Federation, developers can do this without the need to move data across systems and complex data warehouse platforms, providing real-time analytics capabilities that underwriting algorithms require.

MongoDB offers a flexible developer data platform that maps to how developers think and code, while allowing data governance when needed. It is strongly consistent and comes with full support for ACID transactions.

Diagram of the MongoDB developer data platform. The platform includes (from top to bottom) the Document Model, Unified Query API, and the Platform Foundation. The features included with the Unified Query API are Transactional, Search, Time Series, Mobile, and Analytical.
Figure 3.   The MongoDB developer data platform.

The MongoDB developer data platform addresses a range of use cases without added complexity, including full-text search, support for storing data at the edge on mobile, data lake, charts, and the ability to deliver real-time analytics without moving data between systems. It also provides developers with a powerful yet simplified query interface suitable for a variety of workloads, enabling polymorphism and idiomatic access.

Thank you to Ainhoa Múgica and Karolina Ruiz Rogelj for their contributions to this post.

Contact us to find out more about how the MongoDB developer data platform can help you streamline your insurance business.